Optimization Model for Machinery Selection of Multi-Crop Farms in Elsuki Agricultural Scheme

Mysara Ahmed Mohamed, Abdalla Noureldin Osman Kheiry, Abbas Elshiekh Rahama, Alameen Alwathig Alameen


The optimization machinery model was developed to aid decision-makers and farm machinery managers in determining the optimal number of tractors, scheduling the agricultural operation and minimizing machinery total costs. For purpose of model verification, validation and application input data was collected from primary & secondary sources from Elsuki agricultural scheme for two seasons namely 2011-2012 and 2013-2014. Model verification was made by comparing the numbers of tractors of Elsuki agricultural scheme for season 2011-2012 with those estimated by the model. The model succeeded in reducing the number of tractors and operation total cost by 23%. The effect of optimization model on elements of direct cost saving indicated that the highest cost saving is reached with depreciation, repair and maintenance (23%) and the minimum cost saving is attained with fuel cost (22%). Sensitivity analysis in terms of change in model input for each of cultivated area and total costs of operations showing that: Increasing the operation total cost by 10% decreased the total number of tractors after optimization by 23% and total cost of operations was also decreased by 23%. Increasing the cultivated area by 10%, decreased the total number of tractors after optimization by(12%) and total cost of operations was also decreased by 12% (16669206 SDG(1111280 $) to 14636376 SDG(975758 $)). For the case of multiple input effect of the area and operation total cost resulted in decrease maximum number of tractors by 12%, and the total cost of operations also decreased by 12%. It is recommended to apply the optimization model as pre-requisite for improving machinery management during implementation of machinery scheduling.


Optimization; Machinery management; Verification; Validation

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DOI: https://doi.org/10.24925/turjaf.v5i7.739-744.1144

 Creative Commons License
This work is licensed under Creative Commons Attribution 4.0 International License

ISSN: 2148-127X

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